
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"


df=pd.read_csv("/Users/abelabykuriakose/downloads/credit_scoring.csv")


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 12 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Age                        1000 non-null   int64  
 1   Gender                     1000 non-null   object 
 2   Marital Status             1000 non-null   object 
 3   Education Level            1000 non-null   object 
 4   Employment Status          1000 non-null   object 
 5   Credit Utilization Ratio   1000 non-null   float64
 6   Payment History            1000 non-null   float64
 7   Number of Credit Accounts  1000 non-null   int64  
 8   Loan Amount                1000 non-null   int64  
 9   Interest Rate              1000 non-null   float64
 10  Loan Term                  1000 non-null   int64  
 11  Type of Loan               1000 non-null   object 
dtypes: float64(3), int64(4), object(5)
memory usage: 93.9+ KB


df.head()


credit_utilization_fig = px.box(df, y='Credit Utilization Ratio',
                                title='Credit Utilization Ratio Distribution')
credit_utilization_fig.show()


loan_amount_fig = px.histogram(df, x='Loan Amount', 
                               nbins=20, 
                               title='Loan Amount Distribution')
loan_amount_fig.show()


import seaborn as sns
import matplotlib.pyplot as plt

numeric_df = df[['Credit Utilization Ratio', 
                   'Payment History', 
                   'Number of Credit Accounts', 
                   'Loan Amount', 'Interest Rate', 
                   'Loan Term']]
corr=numeric_df.corr()

fig_corr_heatmap=sns.heatmap(corr, annot=True, cmap='coolwarm')

plt.show()

/Applications/anaconda3/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning:

A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.2)


# Define the mapping for categorical features
education_level_mapping = {'High School': 1, 'Bachelor': 2, 'Master': 3, 'PhD': 4}
employment_status_mapping = {'Unemployed': 0, 'Employed': 1, 'Self-Employed': 2}

# Apply mapping to categorical features
df['Education Level'] = df['Education Level'].map(education_level_mapping)
df['Employment Status'] = df['Employment Status'].map(employment_status_mapping)

# Calculate credit scores using the complete FICO formula
credit_scores = []

for index, row in df.iterrows():
    payment_history = row['Payment History']
    credit_utilization_ratio = row['Credit Utilization Ratio']
    number_of_credit_accounts = row['Number of Credit Accounts']
    education_level = row['Education Level']
    employment_status = row['Employment Status']

    # Apply the FICO formula to calculate the credit score
    credit_score = (payment_history * 0.35) + (credit_utilization_ratio * 0.30) + (number_of_credit_accounts * 0.15) + (education_level * 0.10) + (employment_status * 0.10)
    credit_scores.append(credit_score)

# Add the credit scores as a new column to the DataFrame
df['Credit Score'] = credit_scores

df


from sklearn.cluster import KMeans

X = df[['Credit Score']]
kmeans = KMeans(n_clusters=4, n_init=10, random_state=42)
kmeans.fit(X)
df['Segment'] = kmeans.labels_


# Convert the 'Segment' column to category data type
df['Segment'] = df['Segment'].astype('category')

# Visualize the segments using Plotly
fig = px.scatter(df, x=df.index, y='Credit Score', color='Segment',
                 color_discrete_sequence=['green', 'blue', 'yellow', 'red'])
fig.update_layout(
    xaxis_title='Customer Index',
    yaxis_title='Credit Score',
    title='Customer Segmentation based on Credit Scores'
)
fig.show()


df['Segment'] = df['Segment'].map({2: 'Very Low', 
                                       0: 'Low',
                                       1: 'Good',
                                       3: "Excellent"})

# Convert the 'Segment' column to category data type
df['Segment'] = df['Segment'].astype('category')

# Visualize the segments using Plotly
fig = px.scatter(df, x=df.index, y='Credit Score', color='Segment',
                 color_discrete_sequence=['green', 'blue', 'yellow', 'red'])
fig.update_layout(
    xaxis_title='Customer Index',
    yaxis_title='Credit Score',
    title='Customer Segmentation based on Credit Scores'
)
fig.show()

	Age	Gender	Marital Status	Education Level	Employment Status	Credit Utilization Ratio	Payment History	Number of Credit Accounts	Loan Amount	Interest Rate	Loan Term	Type of Loan
0	60	Male	Married	Master	Employed	0.22	2685.0	2	4675000	2.65	48	Personal Loan
1	25	Male	Married	High School	Unemployed	0.20	2371.0	9	3619000	5.19	60	Auto Loan
2	30	Female	Single	Master	Employed	0.22	2771.0	6	957000	2.76	12	Auto Loan
3	58	Female	Married	PhD	Unemployed	0.12	1371.0	2	4731000	6.57	60	Auto Loan
4	32	Male	Married	Bachelor	Self-Employed	0.99	828.0	2	3289000	6.28	36	Personal Loan

	Age	Gender	Marital Status	Education Level	Employment Status	Credit Utilization Ratio	Payment History	Number of Credit Accounts	Loan Amount	Interest Rate	Loan Term	Type of Loan	Credit Score
0	60	Male	Married	3	1	0.22	2685.0	2	4675000	2.65	48	Personal Loan	940.516
1	25	Male	Married	1	0	0.20	2371.0	9	3619000	5.19	60	Auto Loan	831.360
2	30	Female	Single	3	1	0.22	2771.0	6	957000	2.76	12	Auto Loan	971.216
3	58	Female	Married	4	0	0.12	1371.0	2	4731000	6.57	60	Auto Loan	480.586
4	32	Male	Married	2	2	0.99	828.0	2	3289000	6.28	36	Personal Loan	290.797
...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	59	Male	Divorced	1	1	0.74	1285.0	8	3530000	12.99	48	Auto Loan	451.372
996	64	Male	Divorced	2	0	0.77	1857.0	2	1377000	18.02	60	Home Loan	650.681
997	63	Female	Single	3	2	0.18	2628.0	10	2443000	18.95	12	Personal Loan	921.854
998	51	Female	Married	4	2	0.32	1142.0	3	1301000	1.80	24	Auto Loan	400.846
999	37	Female	Married	3	2	0.17	1028.0	5	4182000	9.34	24	Auto Loan	361.101